Title :
Fitting superellipses
Author_Institution :
Dept. of Comput. Sci., Cardiff Univ., UK
fDate :
7/1/2000 12:00:00 AM
Abstract :
In the literature, methods for fitting superellipses to data tend to be computationally expensive due to the nonlinear nature of the problem. This paper describes and tests several fitting techniques which provide different trade-offs between efficiency and accuracy. In addition, we describe various alternative error of fit measures that can be applied by most superellipse fitting methods
Keywords :
computer vision; curve fitting; error analysis; image representation; optimisation; accuracy; computer vision; curve fitting; efficiency; error analysis; image representation; optimisation; superellipses; Closed-form solution; Computational modeling; Curve fitting; Industrial control; Layout; Parameter estimation; Performance evaluation; Shape; Simulated annealing; Testing;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
